Evaluating the student performance prediction and action framework through a learning analytics intervention study

IF 4.8 2区 教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH Education and Information Technologies Pub Date : 2024-08-06 DOI:10.1007/s10639-024-12923-5
Khalid Alalawi, Rukshan Athauda, Raymond Chiong, Ian Renner
{"title":"Evaluating the student performance prediction and action framework through a learning analytics intervention study","authors":"Khalid Alalawi, Rukshan Athauda, Raymond Chiong, Ian Renner","doi":"10.1007/s10639-024-12923-5","DOIUrl":null,"url":null,"abstract":"<p>Learning analytics intervention (LAI) studies aim to identify at-risk students early during an academic term using predictive models and facilitate educators to provide effective interventions to improve educational outcomes. A major impediment to the uptake of LAI is the lack of access to LAI infrastructure by educators to pilot LAI, which typically requires substantial institution-wide efforts and investment to collect related data sets and develop accurate predictive models that identify at-risk students and also provide tools to facilitate interventions. This paper presents a novel LAI framework, termed Student Performance Prediction and Action (SPPA), that facilitates educators to seamlessly provide LAIs in their courses avoiding the need for large-scale institution-wide efforts and investments. Educators develop course-specific predictive models using historical course assessment data. In learning analytics, providing effective interventions is a challenge. SPPA utilises pedagogy principles in course design and interventions to facilitate effective interventions by providing insights into students’ risk levels, gaps in students’ knowledge, and personalised study/revision plans addressing knowledge gaps. SPPA was evaluated in a large undergraduate course on its ability to predict at-risk students and facilitate effective interventions as well as its ease of use by academics. The results are encouraging with high performance of predictive models, facilitating effective interventions leading to significant improved educational outcomes with positive feedback and uptake by academics. With its advantages, SPPA has the potential to catalyse and influence wide-scale adoption in LAIs.</p>","PeriodicalId":51494,"journal":{"name":"Education and Information Technologies","volume":null,"pages":null},"PeriodicalIF":4.8000,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Education and Information Technologies","FirstCategoryId":"95","ListUrlMain":"https://doi.org/10.1007/s10639-024-12923-5","RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"EDUCATION & EDUCATIONAL RESEARCH","Score":null,"Total":0}
引用次数: 0

Abstract

Learning analytics intervention (LAI) studies aim to identify at-risk students early during an academic term using predictive models and facilitate educators to provide effective interventions to improve educational outcomes. A major impediment to the uptake of LAI is the lack of access to LAI infrastructure by educators to pilot LAI, which typically requires substantial institution-wide efforts and investment to collect related data sets and develop accurate predictive models that identify at-risk students and also provide tools to facilitate interventions. This paper presents a novel LAI framework, termed Student Performance Prediction and Action (SPPA), that facilitates educators to seamlessly provide LAIs in their courses avoiding the need for large-scale institution-wide efforts and investments. Educators develop course-specific predictive models using historical course assessment data. In learning analytics, providing effective interventions is a challenge. SPPA utilises pedagogy principles in course design and interventions to facilitate effective interventions by providing insights into students’ risk levels, gaps in students’ knowledge, and personalised study/revision plans addressing knowledge gaps. SPPA was evaluated in a large undergraduate course on its ability to predict at-risk students and facilitate effective interventions as well as its ease of use by academics. The results are encouraging with high performance of predictive models, facilitating effective interventions leading to significant improved educational outcomes with positive feedback and uptake by academics. With its advantages, SPPA has the potential to catalyse and influence wide-scale adoption in LAIs.

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
通过学习分析干预研究评估学生成绩预测和行动框架
学习分析干预(LAI)研究旨在利用预测模型在学期早期识别问题学生,并帮助教育工作者提供有效的干预措施,以提高教育成果。采用 LAI 的一个主要障碍是教育工作者无法利用 LAI 基础设施来试行 LAI,这通常需要在整个机构范围内做出大量努力和投资,以收集相关数据集和开发准确的预测模型,从而识别问题学生,并提供促进干预的工具。本文介绍了一种新颖的 LAI 框架,称为 "学生成绩预测与行动(SPPA)",该框架有助于教育工作者在其课程中无缝提供 LAI,从而避免了在全校范围内进行大规模努力和投资的需要。教育工作者利用历史课程评估数据开发针对特定课程的预测模型。在学习分析中,提供有效的干预是一项挑战。SPPA 利用课程设计和干预中的教学原则,通过深入了解学生的风险水平、学生的知识差距以及针对知识差距的个性化学习/复习计划,促进有效干预。在一门大型本科课程中,对 SPPA 进行了评估,以了解其预测高风险学生和促进有效干预的能力,以及学术界使用 SPPA 的便捷性。结果令人鼓舞,预测模型表现出色,促进了有效干预,显著改善了教育成果,并得到了学术界的积极反馈和采纳。凭借其优势,SPPA 有可能促进和影响 LAIs 的广泛采用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Education and Information Technologies
Education and Information Technologies EDUCATION & EDUCATIONAL RESEARCH-
CiteScore
10.00
自引率
12.70%
发文量
610
期刊介绍: The Journal of Education and Information Technologies (EAIT) is a platform for the range of debates and issues in the field of Computing Education as well as the many uses of information and communication technology (ICT) across many educational subjects and sectors. It probes the use of computing to improve education and learning in a variety of settings, platforms and environments. The journal aims to provide perspectives at all levels, from the micro level of specific pedagogical approaches in Computing Education and applications or instances of use in classrooms, to macro concerns of national policies and major projects; from pre-school classes to adults in tertiary institutions; from teachers and administrators to researchers and designers; from institutions to online and lifelong learning. The journal is embedded in the research and practice of professionals within the contemporary global context and its breadth and scope encourage debate on fundamental issues at all levels and from different research paradigms and learning theories. The journal does not proselytize on behalf of the technologies (whether they be mobile, desktop, interactive, virtual, games-based or learning management systems) but rather provokes debate on all the complex relationships within and between computing and education, whether they are in informal or formal settings. It probes state of the art technologies in Computing Education and it also considers the design and evaluation of digital educational artefacts.  The journal aims to maintain and expand its international standing by careful selection on merit of the papers submitted, thus providing a credible ongoing forum for debate and scholarly discourse. Special Issues are occasionally published to cover particular issues in depth. EAIT invites readers to submit papers that draw inferences, probe theory and create new knowledge that informs practice, policy and scholarship. Readers are also invited to comment and reflect upon the argument and opinions published. EAIT is the official journal of the Technical Committee on Education of the International Federation for Information Processing (IFIP) in partnership with UNESCO.
期刊最新文献
Development of a virtual reality creative enhancement system utilizing haptic vibration feedback via electroencephalography Is ChatGPT like a nine-year-old child in theory of mind? Evidence from Chinese writing Analysing factors influencing undergraduates’ adoption of intelligent physical education systems using an expanded TAM The importance of aligning instructor age with learning content in designing instructional videos for older adults Evaluating classroom response systems in engineering education: Which metrics better reflect student performance?
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1